/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.mllib;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.clustering.KMeans;
import org.apache.spark.mllib.clustering.KMeansModel;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;

// $example on$
// $example off$

public class JavaKMeansExample {
    public static void main(String[] args) {
        
        SparkConf conf = new SparkConf().setAppName("JavaKMeansExample");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        
        // $example on$
        // Load and parse data
        String path = "data/mllib/kmeans_data.txt";
        JavaRDD<String> data = jsc.textFile(path);
        JavaRDD<Vector> parsedData = data.map(
                new Function<String, Vector>() {
                    public Vector call(String s) {
                        String[] sarray = s.split(" ");
                        double[] values = new double[sarray.length];
                        for (int i = 0; i < sarray.length; i++) {
                            values[i] = Double.parseDouble(sarray[i]);
                        }
                        return Vectors.dense(values);
                    }
                }
        );
        parsedData.cache();
        
        // Cluster the data into two classes using KMeans
        int numClusters = 2;
        int numIterations = 20;
        KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations);
        
        System.out.println("Cluster centers:");
        for (Vector center : clusters.clusterCenters()) {
            System.out.println(" " + center);
        }
        double cost = clusters.computeCost(parsedData.rdd());
        System.out.println("Cost: " + cost);
        
        // Evaluate clustering by computing Within Set Sum of Squared Errors
        double WSSSE = clusters.computeCost(parsedData.rdd());
        System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
        
        // Save and load model
        clusters.save(jsc.sc(), "target/org/apache/spark/JavaKMeansExample/KMeansModel");
        KMeansModel sameModel = KMeansModel.load(jsc.sc(),
                "target/org/apache/spark/JavaKMeansExample/KMeansModel");
        // $example off$
        
        jsc.stop();
    }
}
